Detecting misinformation threads is crucial to guarantee a healthy
environment on social media. We address the problem using the data set created
during the COVID-19 pandemic. It contains cascades of tweets discussing
information weakly labeled as reliable or unreliable, based on a previous
evaluation of the information source. The models identifying unreliable threads
usually rely on textual features. But reliability is not just what is said, but
by whom and to whom. We additionally leverage on network information. Following
the homophily principle, we hypothesize that users who interact are generally
interested in similar topics and spreading similar kind of news, which in turn
is generally reliable or not. We test several methods to learn representations
of the social interactions within the cascades, combining them with deep neural
language models in a Multi-Input (MI) framework. Keeping track of the sequence
of the interactions during the time, we improve over previous state-of-the-art
models.

在 COVID-19 疫情期间，使用多输入（MI）框架将社交媒体文本特征和网络信息特征结合起来，提高了检测不可靠信息的模型的准确性。

利用社交互动识别社交媒体上的错误信息

Leveraging Social Interactions to Detect Misinformation on Social Media

Active research pertaining to the affective phenomenon of empathy and
distress is invaluable for improving human-machine interaction. Predicting
intensities of such complex emotions from textual data is difficult, as these
constructs are deeply rooted in the psychological theory. Consequently, for
better prediction, it becomes imperative to take into account ancillary factors
such as the psychological test scores, demographic features, underlying latent
primitive emotions, along with the text's undertone and its psychological
complexity. This paper proffers team PVG's solution to the WASSA 2021 Shared
Task on Predicting Empathy and Emotion in Reaction to News Stories. Leveraging
the textual data, demographic features, psychological test score, and the
intrinsic interdependencies of primitive emotions and empathy, we propose a
multi-input, multi-task framework for the task of empathy score prediction.
Here, the empathy score prediction is considered the primary task, while
emotion and empathy classification are considered secondary auxiliary tasks.
For the distress score prediction task, the system is further boosted by the
addition of lexical features. Our submission ranked 1$^{st}$ based on the
average correlation (0.545) as well as the distress correlation (0.574), and
2$^{nd}$ for the empathy Pearson correlation (0.517).

本研究使用多输入、多任务框架，对情感评分、情感和共情分类的 WASSA 2021 共享任务进行了解决方案，并将文本数据、心理测验得分、人口统计特征和原始情绪及共情的内在相互依赖性纳入考虑范畴，进而在困难的文本数据中预测了这些情绪的强度。